Monkeypox virus shows potential to infect a diverse range of native animal species across Europe, indicating high risk of becoming endemic in the region (preprint)

Background: Monkeypox is a zoonotic virus which persists in animal reservoirs and periodically spills over into humans, causing outbreaks. During the current 2022 outbreak, monkeypox virus has persisted via human-human transmission, across all major continents and for longer than any previous record. This unprecedented spread creates the potential for the virus to "spillback" into local susceptible animal populations. Persistent transmission amongst such animals raises the prospect of monkeypox virus becoming enzootic in new regions. However, the full and specific range of potential animal hosts and reservoirs of monkeypox remains unknown, especially in newly at-risk non-endemic areas. Methods: Here, utilising ensembles of classifiers comprising different class balancing techniques and incorporating instance weights, we identify which animal species are potentially susceptible to monkeypox virus. Subsequently, we generate spatial distribution maps to highlight high-risk geographic areas at high resolution. Findings: We show that the number of potentially susceptible species is currently underestimated by 2.4 to 4.3-fold, and that a high density of wild susceptible species are native to Europe. We provide lists of these species, and highlight high-risk hosts for spillback and potential long-term reservoirs, which may enable monkeypox virus to become endemic. Interpretation: We highlight the European red fox and brown rat, as they have established interactions with potentially contaminated urban waste and sewage, which provides a mechanism for potential spillback. We anticipate that our results will enable targeted active surveillance of potential spillback event, to minimise risk of the virus becoming endemic in these regions. Our results also indicate the potential of domesticated cats and dogs (latter now confirmed) being susceptible to monkeypox virus, and hence support many health organisations' advice for infected humans to avoid physical interaction with pets.

Assessing the impact of non-pharmaceutical interventions on the dynamics of COVID-19: A mathematical modelling study in the case of Ethiopia (preprint)

The World Health Organisation (WHO) declared COVID-19 a pandemic on March 11, 2020 and by November 14, 2020 there were 53.3M confirmed cases and 1.3M reported deaths in the world. In the same period, Ethiopia reported 102K cases and 1.5K deaths. Effective public health preparedness and response to COVID-19 requires timely projections of the time and size of the peak of the outbreak. Currently, in the absence of vaccine or effective treatment, the implementation of NPIs (non-pharmaceutical interventions), like hand washing, wearing face coverings or social distancing, is recommended by WHO to bring the pandemic under control. This study proposes a modified Susceptible Exposed Infected and Recovered (SEIR) model to predict the number of COVID-19 cases at different stages of the disease under the implementation of NPIs with different adherence levels in both urban and rural settings of Ethiopia. To estimate the number of cases and their peak time, 30 different scenarios were simulated. The results reveal that the peak time of the pandemic is different in urban and rural populations of Ethiopia. In the urban population, under moderate implementation of three NPIs the pandemic will be expected to reach its peak in December, 2020 with 147,972 cases, of which 18,100 are symptomatic and 957 will require admission to an Intensive Care Unit (ICU). Among the implemented NPIs, increasing the coverage of wearing masks by 10% could reduce the number of new cases on average by one-fifth in urban-populations. Varying the coverage of wearing masks in rural populations minimally reduces the number of cases. In conclusion, the projection result reveals that the projected number of hospital cases is higher than the Ethiopian health system capacity during the peak time. To contain symptomatic and ICU cases within health system capacity, the government should give attention to the strict implementation of the existing NPIs or impose additional public health measures.

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide (preprint)

We are now over seven months into a pandemic of COVID-19 caused by the SARS-CoV-2 virus and global incidence continues to rise. In some regions such as the temperate northern hemisphere there are fears of "second waves" of infections over the coming months, while in other, vulnerable regions such as Africa and South America, concerns remain that cases may still rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate and seasonality observed for other common respiratory viruses such as seasonal influenza. Here we investigate any empirical evidence of seasonality using a robust estimation framework. For 304 large cities across the world, we estimated the basic reproduction number (R0) using logistic growth curves fitted to cumulative case data. We then assessed evidence for association with climatic variables through mixed-effects and ordinary least squares (OLS) regression while adjusting for city-level variation in demographic and disease control factors. We find evidence of association between temperature and R0 during the early phase of the epidemic in China only. During subsequent pandemic spread outside China, we instead find evidence of seasonal change in R0, with greater R0 within cities experiencing shorter daylight hours (direct effect coefficient = -0.247, p = 0.006), after separating out effects of calendar day. The effect of daylight hours may be driven by levels of UV radiation, which is known to have detrimental effects on coronaviruses, including SARS-CoV-2. In the global analysis excluding China, climatic variables had weaker explanatory power compared to demographic or disease control factors. Overall, we find a weak but detectable signal of climate variables on the transmission of COVID-19. As seasonal changes occur later in 2020, it is feasible that the transmission dynamics of COVID-19 may shift in a detectable manner. However, rates of transmission and health burden of the pandemic in the coming months will be ultimately determined by population factors and disease control policies.

Predicting mammalian hosts in which novel coronaviruses can be generated. (preprint)

Novel pathogenic coronaviruses - including SARS-CoV and SARS-CoV-2 - arise by homologous recombination in a host cell1,2. This process requires a single host to be infected with more than one type of coronavirus, which recombine to form novel strains of virus with unique combinations of genetic material. Identifying possible sources of novel coronaviruses requires identifying hosts (termed recombination hosts) of more than one coronavirus type, in which recombination might occur. However, the majority of coronavirus-host interactions remain unknown, and therefore the vast majority of recombination hosts for coronaviruses cannot be identified. Here we show that there are 11.5-fold more coronavirus-host associations, and over 30-fold more potential SARS-CoV-2 recombination hosts, than have been observed to date. We show there are over 40-fold more host species with four or more different subgenera of coronaviruses. This underestimation of both number and novel coronavirus generation in wild and domesticated animals. Our results list specific high-risk hosts in which our model predicts homologous recombination could occur, our model identifies both wild and domesticated mammals including known important and understudied species. We recommend these species for coronavirus surveillance, as well as enforced separation in livestock markets and agriculture.